The Simplicity and Power model for inductive inference

Anderson JR, Bothell D, Byrne MD, Douglass S, Lebiere C and Qin Y (2004). An integrated theory of the mind. Psychol Rev 111: 1036–1060

Baxter R, Oliver J (1994) MDL and MML: similarities and differences. Tech Report 207, Department of Computer Science, Monash University

Boucher L and Dienes Z (2003). Two ways of learning associations. Cogn Sci 27: 807–842

Chater N (1996). Reconciling simplicity and likelihood principles in perceptual organization. Psychol Rev 103: 566–591

Chater N (1997) Simplicity and the mind. The psychologist. November 1997:495–498

Chater N (1999). The search for simplicity: a fundamental cognitive principle?. Quart J Exp Psychol 52A: 273–302

Chater N, Hahn U (1997) Representational distortion, similarity and the Universal Law of generalization. In: Proceedings of the similarity and categorization workshop 97. University of Edinburgh, pp 31–36

Chater N and Manning CD (2006). Probabilistic models of language processing and acquisition. Trends Cogn Sci 10: 335–344

Chater N and Oaksford M (1999). The probability heuristics model of syllogistic reasoning. Cogn Psychol 38: 191–258

Feldman J (2004). How surprising is a simple pattern? Quantifying “Eureka!”. Cognition 93: 199–224

Garner WR (1974). The processing of information and structure. LEA, Potomac, Md

Grunwald PD, Myung J, Pitt MA (eds) (2005). Advances in minimum description length: theory and applications. Cambridge, MIT Press

Hahn U and Chater N (1998). Similarity and rules: distinct? exhaustive? empirically distinguishable?. Cognition 65: 197–230

Hahn U, Bailey TM and Elvin LBC (2005). Effects of category diversity on learning, memory, and generalization. Mem Cogn 33: 289–302

Hines P, Pothos EM, Chater N (in press) A non-parametric approach to simplicity clustering. Appl Artif Intell

Hochberg JE and McAlister E (1953). A quantitative approach to figural goodness. J Exp Psychol 46: 361–364

Howson C and Urbach P (1993). Scientific reasoning: the Bayesian approach. Open Court, Chicago

Knowlton BJ and Squire LR (1996). Artificial grammar learning depends on implicit acquisition of both abstract and exemplar-specific information. J Exp Psychol: Learn, Mem Cogn 22: 169–181

LiM Vitanyi P (1997). An introduction to Kolmogorov complexity and its applications, 2nd edn. Springer-Verlag, Berlin

Logan GD (1988). Toward an instance theory of automatization. Psychol Rev 95: 492–527

Love BC, Medin DL and Gureckis TM (2004). SUSTAIN: a network model of category learning. Psychol Rev 111: 309–332

Mareschal D, Quinn PC and French RM (2002). Asymmetric interference in 3- to 4-month-olds’ sequential category learning. Cogn Sci 26: 377–389

Marr D (1982). Vision: a computational investigation into the human representation and processing of visual information. W. H. Freeman, San Francisco

Meulemans T, van der Linden M (1997). Associative chunk strength in artificial grammar learning. J Exp Psychol: Learn, Mem, Cogn 23: 1007–1028

Miller GA (1958). Free recall of redundant strings of letters. J Exp Psychol 56: 485–491

Norenzayan A and Heine SJ (2005). Psychological universals: what are they and how can we know?. Psychol Bull 131: 763–784

Nosofsky RM (1989). Further tests of an exemplar-similarity approach to relating identification and categorization. J Exp Psychol: Percept Psychophys 45: 279–290

Oaksford M and Chater N (1991). Against logicist cognitive science. Mind Lang 6: 1–38

Oaksford M and Chater N (1994). A rational analysis of the selection task as optimal data selection. Psychol Rev 101: 608–631

Oaksford M, Chater N, Grainger B and Larkin J (1997). Optimal data selection in the reduced array selection task (RAST). J Exp Psychol: Learn, Mem, Cogn 23: 441–458

Perruchet P, Vinter A, Pacteau C and Gallego J (2002). The formation of structurally relevant units in artificial grammar learning. Quart J Exp Psychol 55A: 485–503

Pomerantz JR and Kubovy M (1986). Theoretical approaches to perceptual organization: simplicity and likelihood principles. In: Boff, KR, Kaufman, L and Thomas, JP (eds) Handbook of perception and human performance. vol. II, pp 1–45. Wiley, Cognitive processes and performance

Posner MI and Keele SW (1968). On the genesis of abstract ideas. J Exp Psychol 77: 353–363

Pothos EM (2005). The rules versus similarity distinction. Behav Brain Sci 28: 1–49

Pothos EM, Bailey TM (1999) An entropy model of artificial grammar learning. In: Proceedings of the twenty-first annual conference of the cognitive science society, LEA, Mahwah, pp 549–554

Pothos EM and Bailey TM (2000). The importance of similarity in artificial grammar learning. J Exp Psychol: Learn, Mem, Cogn 26: 847–862

Pothos EM and Chater N (2002). A simplicity principle in unsupervised human categorization. Cogn Sci 26: 303–343

Pothos EM and Chater N (2005). Unsupervised categorization and category learning. Quart J Exp Psychol 58A: 733–752

Pothos EM and Ward R (2000). Symmetry, repetition and figural goodness: an investigation of the weight of evidence theory. Cognition 75: B65–B78

Reber AS (1989). Implicit learning and tacit knowledge. J Exp Psychol: General 118: 219–235

Regehr G and Brooks LR (1993). Perceptual manifestations of an analytic structure: the priority of holistic individuation. J Exp Psychol: General 122: 92–114

Rips LJ (1989). Similarity, typicality and categorization. In: Vosniadou S, Ortony A (eds) Similarity and analogical reasoning. Cambridge University Press, Cambridge

Rissanen J (1978). Modeling by shortest data description. Automatica 14: 465–471

Rissanen J (1987). Stochastic complexity. J Royal Stat Soc Ser B 49: 223–239

Rissanen J (1989). Stochastic complexity and statistical inquiry. World Scientific, Singapore

Rouder JN and Ratcliff R (2006). Comparing exemplar- and rule-based theories of categorization. Curr Direction Psychol Sci 15: 9–13

Shin HJ and Nosofsky RM (1992). Similarity-scaling studies of “dot-pattern” classification and recognition. J Exp Psychol: General 121: 278–304

Smith EE and Sloman SA (1994). Similarity—vs. rule—based categorization. Mem Cogn 22: 377–386

Solomonoff RJ (1964) A formal theory of inductive inference. Parts I and II. Inf Control 7:1–22, 224–254

Solomonoff RJ (1978). Complexity-based induction systems: comparisons and convergence theorems. IEEE Trans Inf Theory 24: 422–432

Tenenbaum J and Griffiths TL (2001). Generalization, similarity and Bayesian inference. Behav Brain Sci 24: 629–641

Tversky A (1977). Features of similarity. Psychol Rev 84: 327–352

Vitanyi PMB, Li M (1997) On prediction by data compression. In: Proceedings of 9th European conference on machine learning, lecture notes in artificial intelligence, vol 1224. Springer-Verlag, Heidelberg, pp 14–30

Leeuwenberg LJ and Helm PA (1996). Goodness of visual regularities: A nontransformational approach. Psychol Rev 103: 429–456

Vokey JR and Brooks LR (1992). Salience of item knowledge in learning artificial grammar. J Exp Psychol: Learn, Mem Cogn 20: 328–344

Wallace CS and Boulton DM (1968). An information measure for classification. Comp J 11: 185–195

Wallace CS and Freeman PR (1987). Estimation and inference by compact coding. J R Stat Soc, Ser B 49: 240–251

Wasserman EA and Miller RR (1997). What’s elementary about associative learning?. Ann Rev Psychol 48: 573–607

Wolff JG (2003). Information compression by multiple alignment, unification and search as a unifying principle in computing and cognition. Artif Intell Rev 19(3): 193–230

Wolff JG (2006) Unifying computing and cognition: the SP theory and its applications. CognitionResearch.org.uk, Menai Bridge. ISBN 0–9550726-0-3 (Ebook edition distributed by Amazon.com